An image processing pipeline to segment iris for unconstrained cow identification system

One of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.Fil: Larregui, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Cazzato, Dario. : University Of Luxembourg; Luxemburgo. Interdisciplinary Centre For Security Reliability And T; LuxemburgoFil: Castro, Silvia Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin

Cattle Identification Using Muzzle Images and Deep Learning Techniques

Traditional animal identification methods such as ear-tagging, ear notching, and branding have been effective but pose risks to the animal and have scalability issues. Electrical methods offer better tracking and monitoring but require specialized equipment and are susceptible to attacks. Biometric identification using time-immutable dermatoglyphic features such as muzzle prints and iris patterns is a promising solution. This project explores cattle identification using 4923 muzzle images collected from 268 beef cattle. Two deep learning classification models are implemented - wide ResNet50 and VGG16\_BN and image compression is done to lower the image quality and adapt the models to work for the African context. From the experiments run, a maximum accuracy of 99.5\% is achieved while using the wide ResNet50 model with a compression retaining 25\% of the original image. From the study, it is noted that the time required by the models to train and converge as well as recognition time are dependent on the machine used to run the model.Comment: 8 pages, 4 figures, 2 table

Iris recognition method based on segmentation

The development of science and studies has led to the creation of many modern means and technologies that focused and directed their interests on enhancing security due to the increased need for high degrees of security and protection for individuals and societies. Hence identification using a person's vital characteristics is an important privacy topic for governments, businesses and individuals. A lot of biometric features such as fingerprint, facial measurements, acid, palm, gait, fingernails and iris have been studied and used among all the biometrics, in particular, the iris gets the attention because it has unique advantages as the iris pattern is unique and does not change over time, providing the required accuracy and stability in verification systems. This feature is impossible to modify without risk. When identifying with the iris of the eye, the discrimination system only needs to compare the data of the characteristics of the iris of the person to be tested to determine the individual's identity, so the iris is extracted only from the images taken. Determining correct iris segmentation methods is the most important stage in the verification system, including determining the limbic boundaries of the iris and pupil, whether there is an effect of eyelids and shadows, and not exaggerating centralization that reduces the effectiveness of the iris recognition system. There are many techniques for subtracting the iris from the captured image. This paper presents the architecture of biometric systems that use iris to distinguish people and a recent survey of iris segmentation methods used in recent research, discusses methods and algorithms used for this purpose, presents datasets and the accuracy of each method, and compares the performance of each method used in previous studie

Identification and recognition of animals from biometric markers using computer vision approaches: a review

Although classic methods (such as ear tagging, marking, etc.) are generally used for animal identification and recognition, biometric methods have gained popularity in recent years due to the advantages they offer. Systems utilizing biometric markers have been developed for various purposes in animal management, including more effective and accurate tracking of animals, vaccination, disease management, and prevention of theft and fraud. Animals" irises, retinas, faces, muzzle, and body patterns contain unique biometric markers. The use of these markers in computer vision approaches for animal identification and tracking systems has become a highly effective and promising research area in recent years. This review aims to provide a general overview of the latest developments in image processing approaches for animal identification and recognition applications. In this review, we examined in detail all relevant studies we could access from different electronic databases for each biometric method. Afterward, the opportunities and challenges of classical and biometric methods were compared. We anticipate that this study, which conducts a literature review on animal identification and recognition based on computer vision approaches, will shed light on future research towards developing automated systems with biometric methods

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication

The Development of an e-Traceability System for Cattle Delivery Chains

Transparency of livestock supply chain management is still a significant problem in Indonesia due to the unavailability of data and information accessible by the stakeholders of cattle supply chains. It is difficult to obtain information for queries, monitoring, and control purposes at any node along cattle supply chains, and thus introducing some risks of insecurity and uncertainty of cattle conditions along the supply chains. Nowadays, consumers are getting smarter and more curious about selecting healthy and high-quality beef. This requires the provision of an easily and securely accessible traceability and transparency system. The aim of this research is to develop an e-traceability system for cattle supply chains. The proposed e-traceability system was developed on the basis of a web-platform that provides wide access and easy links to all actors within a cattle supply chain and stakeholders. All actors in the cattle supply chain need to be registered and the data related to cattles need to be recorded in the traceability system database for analytic and decision-making. The potential applicability of the developed e-traceability system are examined and demonstrated to highlight the benefits of the system in improving transparency and traceability cattle deliveries from land to table for better managerial tasks

PICES Press, Vol. 21, No. 1, Winter 2013

•2012 PICES Science: A Note from the Science Board Chairman (pp. 1-6) ◾2012 PICES Awards (pp. 7-9) ◾GLOBEC/PICES/ICES ECOFOR Workshop (pp. 10-15) ◾ICES/PICES Symposium on “Forage Fish Interactions” (pp. 16-18) ◾The Yeosu Declaration, the Yeosu Declaration Forum and the Yeosu Project (pp. 19-23) ◾2013 PICES Calendar (p. 23) ◾Why Do We Need Human Dimensions for the FUTURE Program? (pp. 24-25) ◾New PICES MAFF-Sponsored Project on “Marine Ecosystem Health and Human Well-Being” (pp. 26-28) ◾The Bering Sea: Current Status and Recent Trends (pp. 29-31) ◾Continuing Cool in the Northeast Pacific Ocean (pp. 32, 35) ◾The State of the Western North Pacific in the First Half of 2012 (pp. 33-35) ◾New Leadership in PICES (pp. 36-39

MONITORING DAIRY COW FEED INTAKE USING MACHINE VISION

The health and productive output of dairy cows can be closely correlated to individual cow feed intake. Being able to monitor feed intake on a daily basis is beneficial dairy farm management. Each cow can be addressed individually with minimal time required from those working with the animals. This is essential as time management is closely tied to resource management in a dairy operation. Anything that can save time and resources and increase profitability and herd health is a paramount advantage in dairy farming. This study examined the use of machine vision structured light illumination three-dimensional scanning of cow feed to determine the volume and weight of feed in a bin before and after feeding dairy cow. Calibration and control tests were conducted to determine the effectiveness and capability of implementing such a machine vision feed scanning system. Such a system is ideal as it does not obstruct workflow or cow feeding behavior. This is an improvement over existing systems as the system in this research study can be implemented into existing farm operations with minimal effort and costs